- Here are the top data science career paths for you to choose from:
- 1. Data Scientist
- 2. Data Analyst
- 3. Data Engineer
- 4. Data Architect
- 5. Business Intelligence Analyst
- 6. Statistician
- What Next?
- Introduction
- What are the benefits of a Data Science Career?
- Learn the fundamentals
- Choose a Language/tool
- Effective communication skills
- End Note
Data Science continues to be the number one job in the market today. As per the US Bureau of Labor Statistics reports1, it has been estimated that data science skills will offer a 27.9% rise in employment by 2026. This shows how important data science skills are going to be in the coming years.
If we have a look at LinkedIn today, there are 259,188 data science jobs across the world. With such high demand for this skill, what data science career path is right for you?
In this article, we’ll tell you just that.
Here are the top data science career paths for you to choose from:
1. Data Scientist
The most in-demand data science career path chosen by professionals is that of a data scientist. If you want to establish a career as a data scientist, you will be responsible for the collection, and analysis of data and get actionable insights based on a large amount of raw data. If you wish to choose this data science career path, you need to incline towards the tech side of things which involves a strong background in mathematics, stats, and programming skills.
AVERAGE SALARY2: The national average salary for a Data Scientist is $1,13,309 in the United States.
Also Read: Data Scientist Salary Trends in 2022
2. Data Analyst
A Data Analyst basically collects information from various sources and analyzes its patterns to gain actionable insights. They are responsible for transforming and manipulating large data sets to match the desired analysis for the companies. A data analyst recommends the different methods and techniques which can help a company in improving the quality of data systems.
AVERAGE SALARY: The national average salary for a Data Analyst is $62,453 in the United States.
3. Data Engineer
This is one of the other famous data science career paths that attract multiple professionals. A Data Engineer is responsible for creating and nurturing data pipelines that help in making information available to data scientists. They are also responsible for building new APIs to support the increase in data complexity. They work in close contact with front-end and back-end developers, product managers, and analysts.
AVERAGE SALARY: The national average salary for a Data Engineer is $1,02,864 in the United States.
4. Data Architect
They are the ones who create a blueprint for all the data management systems. Each and every company’s database needs to be built and maintained by identifying all possible structural and installation solutions. This task is performed by a data architect. Their job is to ensure that their company’s data solutions are built for performance and also to design analytics for multiple platforms.
AVERAGE SALARY: The national average salary for a Data Architect is $1,08,278 in the United States.
5. Business Intelligence Analyst
They transform data into insights that ultimately drive business value. This data science career path is for people with strong critical thinking and problem-solving abilities. BI analysts make use of data analytics, data visualization, and data modeling techniques. BI analysts can figure out trends that can help managers in making business decisions to improve the organization’s overall performance.
AVERAGE SALARY: The national average salary for a Business Intelligence Analyst is $76,402 in the United States.
6. Statistician
A statistician has a strong eye for detecting patterns in data. They are mathematics and statistics experts who apply statistical methods to solve real-world problems. They are the ones who are responsible for data collection, finding the trends and connections in data, and also communicating the results to stakeholders.
AVERAGE SALARY: The national average salary for a Statistician is $76,884 in the United States.
What Next?
As you would be aware of where you fit in – based on your interests, it is important to move ahead with the choice of your data science career path by taking up a comprehensive program. The Post Graduate Program in Data Science & Business Analytics offered by the McCombs School of Business at The University of Texas at Austin is offered by one of the top universities in the US.
With its well-defined structure, comprehensive curriculum, live and interactive mentored learning, hands-on projects, career services, e-portfolio, and university credibility, this program will put you many steps ahead compared to others and make you job-ready.
Still, got questions?
Get your questions answered by downloading the brochure, and equip yourself to launch your career in data science.
Another top data science course is the Applied Data Science Program, a 12-week program by MIT Professional Education that will help you become a data-driven decision-maker. It offers live virtual teaching from MIT instructors, hands-on projects, and mentorship from industry practitioners. Click here to download the program brochure.
Data science has created a buzz all over the world. It is a stream that deals with analyzing, modeling, and generating data, and deriving meaningful insights and information from this data. The data available to us today is increasing rapidly due to the increase in digitization. Organisations have realized the potential of data and the urgency to analyze this data to gain valuable insights from the same.
To help an organization convert this potential data into a real opportunity, organisations require skilled professionals such as data engineers, data scientists, and AI engineers. Data scientists can mutually benefit from this since they can enhance their skills and knowledge while turning data into real-world success.
Shortage of Skilled Resources
The US will face a shortage of close to 190,000 Data Scientists and 1.5 million managers and analysts, suggests a study by McKinsey Global Institute. There is a high demand for data scientists in India too. Technologies and tools have been introduced, but there is a gap in the number of skilled professionals available to take up these jobs and work on these tools. The co-founder and CEO of Fractal Analytics, Srikanth Velamakanni, states that there are two types of talent deficits. There are data scientists who do not have analytical skills or there are analysts who do not know how to appropriately use data. The demand for skilled professionals in these job roles is in high demand.
Exploiting the Power of Data
One of the main reasons why data scientists are in high demand is due to the growing potential of gathering data from the physical world. With 5G and other such networks that are coming up, digitization across all industries is growing. 5G is expanding and will also be helpful in bringing information to the supporting engineers and operators. Data that was previously available and accessible only on work PCs can now easily be streamed on the shop floor.
There are similar trends across different industries. For example, in the healthcare industry, there are more connectivity and data-collection capabilities when compared to ever before. This creates an unprecedented opportunity for Data Science to flourish.
An Impressive Pay Scale
Currently, a Data Scientist has one of the highest paying jobs in the industry. The future of Data Science is bright, and in the US, the average salary for a data scientist is US$122,525 per year according to Indeed. The salaries vary depending on a number of factors such as job location, hiring company, work experience, and more.
Highest paying cities in the US for Data Scientists
| City | Average Salary (per year) |
| San Francisco, CA | $163,477 |
| New York, NY | $139,774 |
| Austin, TX | $131,133 |
| Los Angeles, CA | $127,028 |
| Chicago, IL | $122,438 |
| Redmond, WA | $121,827 |
| Atlanta, GA | $119,300 |
A Plethora of Job Roles
While Data Science is a broad topic under which there are several smaller and well-defined areas, there are a number of jobs that you can take up. Some of these job roles are a data analyst, data scientist, business analyst, database administrator, and analytics manager.
Also Read: Top 9 Job Roles in the World of Data Science for 2022
There is quite a lot of difference between working in universities and educational institutions and working on real-time projects and case studies. Testing and training your machine learning models on data sets are completely different from performing any research. The input is known as data, and the output depends on the precision and accuracy of this data. Academic research is the backbone of the applications we use on a daily basis. However, in real-world applications and tools that are being used every day, many challenges do come up. It may be difficult to gather data. The data may not exist, and a Data Scientist would have to work with several different database engineers to create the perfect machine learning models to be trained and tested.
Post Graduate Program in Data Science & Business Analytics or PGP-DSBA is a course offered by the McCombs School of Business at The University of Texas at Austin and delivered to you by Great Learning. The PGP- DSBA is a top business analytics program and will teach you comprehensive curriculum that covers the most widely used tools and techniques in the world of Data Science and Analytics.
The Post Graduate Program in Data Science and Business Analytics provides a structured learning approach. You will have the opportunity to interact with industry experts, thus benefitting from their rich professional experience. You will also work on real-time projects and case studies. The course duration is 6-months, and it will empower you to improve yourself. PGP- DSBA also offers practice sessions with mentors, assignments, quizzes, and projects. You can add these projects to your portfolio and also share it on your social channels.
Introduction
Data Science continues to be one of the promising and in-demand careers for skilled professionals. A data science career is rewarding and lucrative, but the way to start a career in data science is not that simple. A bachelor’s degree or a master is not required to become a data science professional. The right skill set and experience are what one needs.
This blog will help you understand the career pathways and required skills and how to start a bright career in data science.
To start a successful career in data science, you just need to have hard skill sets like analysis, machine learning, statistics, neural networks, etc. and you must be a problem solver, critical thinker, and a good storyteller to excel in data science.
Step-by-Step Guide to Becoming a Data Scientist
What are the benefits of a Data Science Career?
The demand for data science skills has skyrocketed, this is due to the numerous amounts of data that is getting generated across the businesses and the need for effective data-driven decision making.
- High Salary
Work experience is the largest factor in data science salaries; however, the average salary of an entry-level data science professional is higher than the median salary in the U.S.
- Solve Complex Problems
The primary job of a data science professional is to find insights by analyzing and processing a huge amount of raw data. Example: How to increase sales? To identify the hidden problems in current business operations.
Being able to extract information from data is a very powerful position to be in with data being collected in all aspects of society, ranging from marketing to health and even to sports and entertainment.”
- Less Vulnerable to Job Automation
In general, the more specialized the job (fewer workers are Doing It), the less likely it is to be automated because there is less incentive to invest the resources needed to build a machine capable of the job.
At that point, data science will be an essential skill, but one not limited to a handful of experts, and we will not need anyone trained specifically in data science. I am hopeful for this time because I have repeatedly seen the importance of domain expertise for building effective data science pipelines. If we can put the right tools in the hands of those with experience in a field, then data science can deliver on its promises of increasing efficiency and enabling objective decisions. Automation is not likely to take data science jobs, but if the right tools are developed, a data scientist may become an extraneous specialization.
Individuals who have extensive knowledge on how to convert data into actionable insights using analytical skills are the ones who have a chance to have a bright career in data science.
Career Paths for Data Science Professionals in 2022
Learn the fundamentals
As a data science professional, you should possess a strong knowledge of the fundamentals of data science like statistics, probability, hypothesis testing, and data analysis tools like Excel which is one of the traditional methods still used to analyze small datasets. You also should be well-versed with programming languages like R, Python, SQL, Hadoop, etc, and data visualization tools like Tableau and Power BI.
You can refer PGP-Data science course offered by the Great learning institute. If you are an aspiring data scientist, you may join the course and upskill with the help of dedicated mentor support.
Choose the right role:
There is a wide range of roles available in data science. A data visualization expert, a machine learning expert, a data scientist, a data engineer, etc., are a few of the many roles that you could go into. Depending on your background and your work experience, getting into one role would be easier than another role. For example, if you are a software developer, it would not be difficult for you to shift into data engineering. So, unless you are clear about what you want to become, you will stay confused about the path to take and skills to hone.
Join data science communities:
The primary role of a data professional is to work alongside internal and external stakeholders to deliver key insights. Outside of that, many data science professionals like to stay connected to their peers working in similar roles to discuss new ideas and methodologies. You can engage yourself with peers in such communities to stay updated with the data science field.
I will take you through the top 3 most popular data science communities.
- Kaggle: It is one of the largest data science communities in the world. It has mainly been built as a tool to evaluate and assemble teams across competitions. It helps you to find and publish data sets, explore and build models in a web-based environment, and share your work with other scientists and engineers. Best of all, the Kaggle community now has more than 3 million active members who can use Kaggle to share their work and expertise.
- IBM data science community: IBM data science web pages are famous because you can find the blogs, and podcasts of some of the legends of the industry. The IBM Data Science Community is a great platform to visit if you are seeking specific guidance from an industry pro. It is a little less newbie-friendly than some of the other communities here, but that is not necessarily a bad thing.
- Reddit: It is a strange community of data science, is better known for sharing memes than addressing complex scientific questions. There are plenty of subreddits largely available for hardcore discussion of papers r/datascience, r/dataisbeautiful, and r/MachineLearning are some of the subreddits widely used.
Choose a Language/tool
This is probably the most asked question by beginners. Which language/ tool should I choose to excel in a career in data science? Before answering this question, I would like to inform you that tools are just meant for implementation, but understanding the concept is more important. The widest and simplest language used in the data science field is python, it is a user-friendly language that helps even a non-technical person to use it with ease. Let me give you a brief idea about the important languages/tools which are currently in demand. Though Microsoft Excel can be used for data analysis, to become a data scientist you should acquire the knowledge of high-level data analysis tools. We will discuss more these tools.
Python:
Python is the most widely used programming language for data science and machine learning and one of the most popular languages overall.
The python language can be used for a wide range of tasks, including data analysis, data visualization, AI, natural language processing, and robotic process automation. Developers can create web, mobile, and desktop applications in Python, too. In addition to object-oriented programming, it supports procedural, functional, and other types, plus extensions written in C or C++.
It is used not only by data scientists, programmers, and network engineers but also by workers outside of computing disciplines, from accountants to mathematicians and scientists, who often are drawn to its user-friendly nature.
R:
The R programming language is an open-source environment designed for statistical computing and graphics applications, as well as data manipulation, analysis, and visualization. Most data scientists, academic researchers, and statisticians use R to retrieve, cleanse, analyze and present data, making it one of the most popular languages for data science and advanced analytics.
Data Visualization Tools:
A data science professional should be a good storyteller, he should have good knowledge in visualization tools to express their views to the audience. Let us see some of the commonly used data visualization tools in the industry.
Tableau:
Tableau is a data visualization and Business Intelligence tool. This visual analytics platform is transforming the way people use data to solve problems. Tableau helps people see and understand data, and make the organizations more data-driven. It is very easy to use and became a leading choice for modern business intelligence. It helps to explore, manage, and discover insights from the data that can change businesses. It is a more powerful, secure, and flexible analytics platform.
There are various products available in tableau like Tableau desktop, server, CRM, etc. one can use as per your requirement.
Power BI:
Power BI is a collection of software services, apps, and connectors that work together to turn your raw data into an interactive insight. The data might be an Excel spreadsheet or a collection of cloud-based and on-premises hybrid data warehouses. Power BI helps you easily connect to your data sources, visualize, and discover what’s important, and share that with anyone or everyone you want. It is one of the most widely used business intelligence tools in the industry.
Effective communication skills
An effective communicator will always be a good storyteller, people expect that if they are technically profound, they will ace the interview for a data science role. This is a myth. You should have good communication skills to convey your thoughts to the audience.
Try to make your friend with good communication skills hear your intro and ask for honest feedback. He will show you the mirror!
With effective communication skills, you can be able to share your ideas with a colleague or prove your point in a meeting. It is an essential skill for anyone who wants to start a career in data science.
End Note
The data science profession is in huge demand and employers are investing significant time and money in the field. By taking the right step you can set a bright career in data science. We hope this blog would have added some value and helped you to understand how to start a bright career in data science.
